Final Report for Intro to Data Science

This is where the final project report write-up goes.

Before you submit, make sure everything runs as expected.

You can add sections as you see fit. Make sure you have a section called “Introduction” at the beginning and a section called “Conclusion” at the end. The rest is up to you!

##Introduction - Load the tidyverse, ggplot, and rtweet packages

library(tidyverse)
library(ggplot2)
library(rtweet)
library(readr)

This data set was scraped from WineEnthusiast, a website that reviews and rates many differet types of wines.

• This dataset includes information of of 130,000 wine reviews with 10 different data fields.

wines <- read.csv(file = '../data/winemag-data-130k-v2.csv')[,-1]

set.seed(19630217)
wine_sample<- sample_n(wines, 1000)

EDA (correlation priceXpoints, with DataExplorer library? using (this)[https://datascienceplus.com/blazing-fast-eda-in-r-with-dataexplorer/])

wines %>% 
  ggplot() +
    geom_point(mapping = (aes(x = points, y = price)), na.rm = T)

wines %>%
      summarize(mean(price, na.rm=TRUE), 
                min(price, na.rm=TRUE),
                max(price,na.rm=TRUE), 
                sd(price, na.rm=TRUE))

wines %>%
      summarize(mean(points, na.rm=TRUE), 
                min(points, na.rm=TRUE),
                max(points,na.rm=TRUE), 
                sd(points, na.rm=TRUE))

Select the provinces based on points and Select the best province for wine based on the average points of the sample size.

#find the average number of points across the 1,000 samples

wine_per_province <- wine_sample %>% 
  select(province, points) %>% 
  summarise(points = mean(points))
wine_per_province

#Find the best province for wine using the average points across the 1,000 samples

best_province <- wine_sample %>% 
  group_by(province, points) %>% 
  filter(points > 88.669)
best_province  

Rating distribution

Bang for your buck, by variety

#sort by price, then points
#want to do an interaction variableor somethin?
#wine_cheap_but_good <- 
wines %>% 
  group_by(variety) %>% 
  summarise(mean(points))# %>% 

#  arrange(points)
  
  

##Conclusion